Overview

Brought to you by YData

Dataset statistics

Number of variables11
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory344.8 KiB
Average record size in memory353.0 B

Variable types

DateTime1
Text2
Categorical2
Numeric6

Alerts

Destination Latitude is highly overall correlated with Destination LongitudeHigh correlation
Destination Longitude is highly overall correlated with Destination LatitudeHigh correlation
Source Latitude is highly overall correlated with Source LongitudeHigh correlation
Source Longitude is highly overall correlated with Source LatitudeHigh correlation
Timestamp has unique values Unique
Source IP has unique values Unique
Destination IP has unique values Unique

Reproduction

Analysis started2025-02-20 15:08:06.154660
Analysis finished2025-02-20 15:08:07.867587
Duration1.71 second
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

Timestamp
Date

Unique 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Minimum2025-01-06 00:00:00
Maximum2025-02-16 15:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-02-20T10:08:07.898968image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:08:07.952240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Source IP
Text

Unique 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size60.9 KiB
2025-02-20T10:08:08.077110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length15
Median length14
Mean length13.219
Min length9

Characters and Unicode

Total characters13219
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1000 ?
Unique (%)100.0%

Sample

1st row68.134.164.72
2nd row109.213.81.120
3rd row204.110.103.23
4th row154.115.196.51
5th row212.148.134.217
ValueCountFrequency (%)
41.42.28.25 1
 
0.1%
211.50.176.100 1
 
0.1%
68.134.164.72 1
 
0.1%
109.213.81.120 1
 
0.1%
204.110.103.23 1
 
0.1%
154.115.196.51 1
 
0.1%
212.148.134.217 1
 
0.1%
2.119.179.102 1
 
0.1%
147.90.85.35 1
 
0.1%
156.157.3.241 1
 
0.1%
Other values (990) 990
99.0%
2025-02-20T10:08:08.245408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 3000
22.7%
1 2530
19.1%
2 1567
11.9%
3 880
 
6.7%
4 842
 
6.4%
5 831
 
6.3%
9 758
 
5.7%
7 720
 
5.4%
0 706
 
5.3%
6 701
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13219
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 3000
22.7%
1 2530
19.1%
2 1567
11.9%
3 880
 
6.7%
4 842
 
6.4%
5 831
 
6.3%
9 758
 
5.7%
7 720
 
5.4%
0 706
 
5.3%
6 701
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13219
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 3000
22.7%
1 2530
19.1%
2 1567
11.9%
3 880
 
6.7%
4 842
 
6.4%
5 831
 
6.3%
9 758
 
5.7%
7 720
 
5.4%
0 706
 
5.3%
6 701
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13219
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 3000
22.7%
1 2530
19.1%
2 1567
11.9%
3 880
 
6.7%
4 842
 
6.4%
5 831
 
6.3%
9 758
 
5.7%
7 720
 
5.4%
0 706
 
5.3%
6 701
 
5.3%

Destination IP
Text

Unique 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size60.9 KiB
2025-02-20T10:08:08.358857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length15
Median length14
Mean length13.201
Min length9

Characters and Unicode

Total characters13201
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1000 ?
Unique (%)100.0%

Sample

1st row26.153.252.160
2nd row185.131.175.1
3rd row3.11.199.135
4th row185.239.128.97
5th row183.188.102.156
ValueCountFrequency (%)
204.65.170.49 1
 
0.1%
196.167.88.231 1
 
0.1%
26.153.252.160 1
 
0.1%
185.131.175.1 1
 
0.1%
3.11.199.135 1
 
0.1%
185.239.128.97 1
 
0.1%
183.188.102.156 1
 
0.1%
148.72.133.221 1
 
0.1%
146.121.244.159 1
 
0.1%
204.210.80.185 1
 
0.1%
Other values (990) 990
99.0%
2025-02-20T10:08:08.510818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 3000
22.7%
1 2477
18.8%
2 1676
12.7%
3 905
 
6.9%
4 839
 
6.4%
5 784
 
5.9%
0 743
 
5.6%
6 717
 
5.4%
9 710
 
5.4%
7 699
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13201
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 3000
22.7%
1 2477
18.8%
2 1676
12.7%
3 905
 
6.9%
4 839
 
6.4%
5 784
 
5.9%
0 743
 
5.6%
6 717
 
5.4%
9 710
 
5.4%
7 699
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13201
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 3000
22.7%
1 2477
18.8%
2 1676
12.7%
3 905
 
6.9%
4 839
 
6.4%
5 784
 
5.9%
0 743
 
5.6%
6 717
 
5.4%
9 710
 
5.4%
7 699
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13201
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 3000
22.7%
1 2477
18.8%
2 1676
12.7%
3 905
 
6.9%
4 839
 
6.4%
5 784
 
5.9%
0 743
 
5.6%
6 717
 
5.4%
9 710
 
5.4%
7 699
 
5.3%

Attack Type
Categorical

Distinct6
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size57.1 KiB
Ransomware
172 
Phishing
172 
SQL Injection
170 
DDoS
167 
Malware
163 

Length

Max length14
Median length10
Mean length9.299
Min length4

Characters and Unicode

Total characters9299
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRansomware
2nd rowSQL Injection
3rd rowSQL Injection
4th rowRansomware
5th rowDDoS

Common Values

ValueCountFrequency (%)
Ransomware 172
17.2%
Phishing 172
17.2%
SQL Injection 170
17.0%
DDoS 167
16.7%
Malware 163
16.3%
Insider Threat 156
15.6%

Length

2025-02-20T10:08:08.555632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T10:08:08.577101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
ransomware 172
13.0%
phishing 172
13.0%
sql 170
12.8%
injection 170
12.8%
ddos 167
12.6%
malware 163
12.3%
insider 156
11.8%
threat 156
11.8%

Most occurring characters

ValueCountFrequency (%)
n 840
 
9.0%
a 826
 
8.9%
e 817
 
8.8%
i 670
 
7.2%
r 647
 
7.0%
o 509
 
5.5%
h 500
 
5.4%
s 500
 
5.4%
S 337
 
3.6%
w 335
 
3.6%
Other values (16) 3318
35.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9299
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 840
 
9.0%
a 826
 
8.9%
e 817
 
8.8%
i 670
 
7.2%
r 647
 
7.0%
o 509
 
5.5%
h 500
 
5.4%
s 500
 
5.4%
S 337
 
3.6%
w 335
 
3.6%
Other values (16) 3318
35.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9299
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 840
 
9.0%
a 826
 
8.9%
e 817
 
8.8%
i 670
 
7.2%
r 647
 
7.0%
o 509
 
5.5%
h 500
 
5.4%
s 500
 
5.4%
S 337
 
3.6%
w 335
 
3.6%
Other values (16) 3318
35.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9299
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 840
 
9.0%
a 826
 
8.9%
e 817
 
8.8%
i 670
 
7.2%
r 647
 
7.0%
o 509
 
5.5%
h 500
 
5.4%
s 500
 
5.4%
S 337
 
3.6%
w 335
 
3.6%
Other values (16) 3318
35.7%

Severity
Categorical

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size53.0 KiB
Medium
262 
Low
258 
High
250 
Critical
230 

Length

Max length8
Median length6
Mean length5.186
Min length3

Characters and Unicode

Total characters5186
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHigh
2nd rowCritical
3rd rowHigh
4th rowMedium
5th rowLow

Common Values

ValueCountFrequency (%)
Medium 262
26.2%
Low 258
25.8%
High 250
25.0%
Critical 230
23.0%

Length

2025-02-20T10:08:08.644654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T10:08:08.676574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
medium 262
26.2%
low 258
25.8%
high 250
25.0%
critical 230
23.0%

Most occurring characters

ValueCountFrequency (%)
i 972
18.7%
M 262
 
5.1%
e 262
 
5.1%
d 262
 
5.1%
u 262
 
5.1%
m 262
 
5.1%
L 258
 
5.0%
o 258
 
5.0%
w 258
 
5.0%
H 250
 
4.8%
Other values (8) 1880
36.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5186
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 972
18.7%
M 262
 
5.1%
e 262
 
5.1%
d 262
 
5.1%
u 262
 
5.1%
m 262
 
5.1%
L 258
 
5.0%
o 258
 
5.0%
w 258
 
5.0%
H 250
 
4.8%
Other values (8) 1880
36.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5186
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 972
18.7%
M 262
 
5.1%
e 262
 
5.1%
d 262
 
5.1%
u 262
 
5.1%
m 262
 
5.1%
L 258
 
5.0%
o 258
 
5.0%
w 258
 
5.0%
H 250
 
4.8%
Other values (8) 1880
36.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5186
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 972
18.7%
M 262
 
5.1%
e 262
 
5.1%
d 262
 
5.1%
u 262
 
5.1%
m 262
 
5.1%
L 258
 
5.0%
o 258
 
5.0%
w 258
 
5.0%
H 250
 
4.8%
Other values (8) 1880
36.3%

Attempt Count
Real number (ℝ)

Distinct19
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.025
Minimum1
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-02-20T10:08:08.708719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median10
Q315
95-th percentile19
Maximum19
Range18
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.5107843
Coefficient of variation (CV)0.54970417
Kurtosis-1.1811198
Mean10.025
Median Absolute Deviation (MAD)5
Skewness0.0032340051
Sum10025
Variance30.368744
MonotonicityNot monotonic
2025-02-20T10:08:08.744623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
19 63
 
6.3%
16 62
 
6.2%
10 60
 
6.0%
8 58
 
5.8%
6 57
 
5.7%
9 56
 
5.6%
12 56
 
5.6%
2 56
 
5.6%
1 55
 
5.5%
11 55
 
5.5%
Other values (9) 422
42.2%
ValueCountFrequency (%)
1 55
5.5%
2 56
5.6%
3 53
5.3%
4 47
4.7%
5 43
4.3%
6 57
5.7%
7 46
4.6%
8 58
5.8%
9 56
5.6%
10 60
6.0%
ValueCountFrequency (%)
19 63
6.3%
18 45
4.5%
17 54
5.4%
16 62
6.2%
15 40
4.0%
14 48
4.8%
13 46
4.6%
12 56
5.6%
11 55
5.5%
10 60
6.0%

Data Volume (MB)
Real number (ℝ)

Distinct628
Distinct (%)62.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean499.045
Minimum2
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-02-20T10:08:08.788862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile50
Q1268.75
median504.5
Q3728
95-th percentile954.1
Maximum999
Range997
Interquartile range (IQR)459.25

Descriptive statistics

Standard deviation282.69606
Coefficient of variation (CV)0.56647408
Kurtosis-1.0870347
Mean499.045
Median Absolute Deviation (MAD)230.5
Skewness0.011353094
Sum499045
Variance79917.062
MonotonicityNot monotonic
2025-02-20T10:08:08.894072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
611 6
 
0.6%
556 6
 
0.6%
328 6
 
0.6%
805 5
 
0.5%
537 5
 
0.5%
34 5
 
0.5%
28 4
 
0.4%
57 4
 
0.4%
594 4
 
0.4%
803 4
 
0.4%
Other values (618) 951
95.1%
ValueCountFrequency (%)
2 1
 
0.1%
5 1
 
0.1%
10 1
 
0.1%
11 1
 
0.1%
12 1
 
0.1%
13 1
 
0.1%
15 2
0.2%
16 3
0.3%
17 1
 
0.1%
18 1
 
0.1%
ValueCountFrequency (%)
999 1
 
0.1%
998 1
 
0.1%
997 4
0.4%
996 2
0.2%
994 2
0.2%
992 2
0.2%
990 2
0.2%
989 2
0.2%
988 2
0.2%
987 1
 
0.1%

Source Latitude
Real number (ℝ)

High correlation 

Distinct15
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.233666
Minimum-37.8136
Maximum55.9533
Zeros0
Zeros (%)0.0%
Negative222
Negative (%)22.2%
Memory size7.9 KiB
2025-02-20T10:08:08.944021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-37.8136
5-th percentile-37.8136
Q134.0522
median40.7128
Q345.5017
95-th percentile55.9533
Maximum55.9533
Range93.7669
Interquartile range (IQR)11.4495

Descriptive statistics

Standard deviation32.093469
Coefficient of variation (CV)1.2233696
Kurtosis-0.29132096
Mean26.233666
Median Absolute Deviation (MAD)6.6606
Skewness-1.2069873
Sum26233.666
Variance1029.9908
MonotonicityNot monotonic
2025-02-20T10:08:08.977644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
-27.4698 89
 
8.9%
34.0522 77
 
7.7%
43.651 72
 
7.2%
35.0116 72
 
7.2%
41.8781 69
 
6.9%
40.7128 69
 
6.9%
-33.8688 68
 
6.8%
49.2827 68
 
6.8%
34.6937 66
 
6.6%
-37.8136 65
 
6.5%
Other values (5) 285
28.5%
ValueCountFrequency (%)
-37.8136 65
6.5%
-33.8688 68
6.8%
-27.4698 89
8.9%
34.0522 77
7.7%
34.6937 66
6.6%
35.0116 72
7.2%
35.6895 56
5.6%
40.7128 69
6.9%
41.8781 69
6.9%
43.651 72
7.2%
ValueCountFrequency (%)
55.9533 58
5.8%
53.4808 53
5.3%
51.5074 61
6.1%
49.2827 68
6.8%
45.5017 57
5.7%
43.651 72
7.2%
41.8781 69
6.9%
40.7128 69
6.9%
35.6895 56
5.6%
35.0116 72
7.2%

Source Longitude
Real number (ℝ)

High correlation 

Distinct15
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.01752
Minimum-123.1207
Maximum153.0251
Zeros0
Zeros (%)0.0%
Negative584
Negative (%)58.4%
Memory size7.9 KiB
2025-02-20T10:08:09.012260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-123.1207
5-th percentile-123.1207
Q1-79.347
median-2.2426
Q3139.6917
95-th percentile153.0251
Maximum153.0251
Range276.1458
Interquartile range (IQR)219.0387

Descriptive statistics

Standard deviation109.47411
Coefficient of variation (CV)5.2087073
Kurtosis-1.730876
Mean21.01752
Median Absolute Deviation (MAD)116.0011
Skewness0.074616537
Sum21017.52
Variance11984.581
MonotonicityNot monotonic
2025-02-20T10:08:09.027008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
153.0251 89
 
8.9%
-118.2437 77
 
7.7%
-79.347 72
 
7.2%
135.7681 72
 
7.2%
-87.6298 69
 
6.9%
-74.006 69
 
6.9%
151.2093 68
 
6.8%
-123.1207 68
 
6.8%
135.5023 66
 
6.6%
144.9631 65
 
6.5%
Other values (5) 285
28.5%
ValueCountFrequency (%)
-123.1207 68
6.8%
-118.2437 77
7.7%
-87.6298 69
6.9%
-79.347 72
7.2%
-74.006 69
6.9%
-73.5673 57
5.7%
-3.1883 58
5.8%
-2.2426 53
5.3%
-0.1278 61
6.1%
135.5023 66
6.6%
ValueCountFrequency (%)
153.0251 89
8.9%
151.2093 68
6.8%
144.9631 65
6.5%
139.6917 56
5.6%
135.7681 72
7.2%
135.5023 66
6.6%
-0.1278 61
6.1%
-2.2426 53
5.3%
-3.1883 58
5.8%
-73.5673 57
5.7%

Destination Latitude
Real number (ℝ)

High correlation 

Distinct15
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.292311
Minimum-37.8136
Maximum55.9533
Zeros0
Zeros (%)0.0%
Negative194
Negative (%)19.4%
Memory size7.9 KiB
2025-02-20T10:08:09.076164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-37.8136
5-th percentile-37.8136
Q134.0522
median40.7128
Q349.2827
95-th percentile55.9533
Maximum55.9533
Range93.7669
Interquartile range (IQR)15.2305

Descriptive statistics

Standard deviation31.183854
Coefficient of variation (CV)1.1022024
Kurtosis0.2327287
Mean28.292311
Median Absolute Deviation (MAD)6.6606
Skewness-1.3904796
Sum28292.311
Variance972.43273
MonotonicityNot monotonic
2025-02-20T10:08:09.111285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
-37.8136 79
 
7.9%
49.2827 78
 
7.8%
34.0522 77
 
7.7%
40.7128 76
 
7.6%
35.0116 69
 
6.9%
41.8781 67
 
6.7%
45.5017 65
 
6.5%
34.6937 65
 
6.5%
35.6895 65
 
6.5%
51.5074 63
 
6.3%
Other values (5) 296
29.6%
ValueCountFrequency (%)
-37.8136 79
7.9%
-33.8688 60
6.0%
-27.4698 55
5.5%
34.0522 77
7.7%
34.6937 65
6.5%
35.0116 69
6.9%
35.6895 65
6.5%
40.7128 76
7.6%
41.8781 67
6.7%
43.651 58
5.8%
ValueCountFrequency (%)
55.9533 62
6.2%
53.4808 61
6.1%
51.5074 63
6.3%
49.2827 78
7.8%
45.5017 65
6.5%
43.651 58
5.8%
41.8781 67
6.7%
40.7128 76
7.6%
35.6895 65
6.5%
35.0116 69
6.9%

Destination Longitude
Real number (ℝ)

High correlation 

Distinct15
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.266275
Minimum-123.1207
Maximum153.0251
Zeros0
Zeros (%)0.0%
Negative607
Negative (%)60.7%
Memory size7.9 KiB
2025-02-20T10:08:09.144267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-123.1207
5-th percentile-123.1207
Q1-79.347
median-2.2426
Q3139.6917
95-th percentile153.0251
Maximum153.0251
Range276.1458
Interquartile range (IQR)219.0387

Descriptive statistics

Standard deviation108.20506
Coefficient of variation (CV)6.6521103
Kurtosis-1.6889094
Mean16.266275
Median Absolute Deviation (MAD)116.0011
Skewness0.14096417
Sum16266.275
Variance11708.334
MonotonicityNot monotonic
2025-02-20T10:08:09.175663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
144.9631 79
 
7.9%
-123.1207 78
 
7.8%
-118.2437 77
 
7.7%
-74.006 76
 
7.6%
135.7681 69
 
6.9%
-87.6298 67
 
6.7%
-73.5673 65
 
6.5%
135.5023 65
 
6.5%
139.6917 65
 
6.5%
-0.1278 63
 
6.3%
Other values (5) 296
29.6%
ValueCountFrequency (%)
-123.1207 78
7.8%
-118.2437 77
7.7%
-87.6298 67
6.7%
-79.347 58
5.8%
-74.006 76
7.6%
-73.5673 65
6.5%
-3.1883 62
6.2%
-2.2426 61
6.1%
-0.1278 63
6.3%
135.5023 65
6.5%
ValueCountFrequency (%)
153.0251 55
5.5%
151.2093 60
6.0%
144.9631 79
7.9%
139.6917 65
6.5%
135.7681 69
6.9%
135.5023 65
6.5%
-0.1278 63
6.3%
-2.2426 61
6.1%
-3.1883 62
6.2%
-73.5673 65
6.5%

Interactions

2025-02-20T10:08:07.525430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:08:06.273221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:08:06.526152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:08:06.765237image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:08:07.063158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:08:07.299371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:08:07.559609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:08:06.318483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:08:06.567151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:08:06.870040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:08:07.092036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:08:07.325510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:08:07.613290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:08:06.360763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:08:06.605943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:08:06.909033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:08:07.144236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:08:07.378412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:08:07.653191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:08:06.392301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:08:06.645228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:08:06.941823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:08:07.176191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:08:07.418244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:08:07.691206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:08:06.441713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:08:06.684222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:08:06.985665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:08:07.221195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:08:07.458565image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:08:07.732399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:08:06.483711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:08:06.724236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:08:07.024426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:08:07.261086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T10:08:07.495800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-02-20T10:08:09.194619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Attack TypeAttempt CountData Volume (MB)Destination LatitudeDestination LongitudeSeveritySource LatitudeSource Longitude
Attack Type1.0000.0400.0000.0290.0330.0000.0000.000
Attempt Count0.0401.000-0.0350.052-0.0210.000-0.0120.000
Data Volume (MB)0.000-0.0351.0000.021-0.0220.0000.017-0.003
Destination Latitude0.0290.0520.0211.000-0.5110.0260.0160.001
Destination Longitude0.033-0.021-0.022-0.5111.0000.0000.015-0.025
Severity0.0000.0000.0000.0260.0001.0000.0000.000
Source Latitude0.000-0.0120.0170.0160.0150.0001.000-0.549
Source Longitude0.0000.000-0.0030.001-0.0250.000-0.5491.000

Missing values

2025-02-20T10:08:07.776202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-20T10:08:07.837541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

TimestampSource IPDestination IPAttack TypeSeverityAttempt CountData Volume (MB)Source LatitudeSource LongitudeDestination LatitudeDestination Longitude
02025-01-06 00:00:0068.134.164.7226.153.252.160RansomwareHigh920840.7128-74.0060-27.4698153.0251
12025-01-06 01:00:00109.213.81.120185.131.175.1SQL InjectionCritical1420-33.8688151.2093-27.4698153.0251
22025-01-06 02:00:00204.110.103.233.11.199.135SQL InjectionHigh16953.4808-2.242635.0116135.7681
32025-01-06 03:00:00154.115.196.51185.239.128.97RansomwareMedium539145.5017-73.567335.0116135.7681
42025-01-06 04:00:00212.148.134.217183.188.102.156DDoSLow10899-27.4698153.025155.9533-3.1883
52025-01-06 05:00:002.119.179.102148.72.133.221RansomwareLow260543.6510-79.3470-33.8688151.2093
62025-01-06 06:00:00124.178.141.18071.110.106.202Insider ThreatLow47834.6937135.502345.5017-73.5673
72025-01-06 07:00:0032.110.204.146179.36.153.217SQL InjectionMedium1139345.5017-73.567340.7128-74.0060
82025-01-06 08:00:0041.42.28.25204.65.170.49DDoSCritical367434.0522-118.243741.8781-87.6298
92025-01-06 09:00:0041.219.100.18335.160.133.130MalwareMedium116240.7128-74.006051.5074-0.1278
TimestampSource IPDestination IPAttack TypeSeverityAttempt CountData Volume (MB)Source LatitudeSource LongitudeDestination LatitudeDestination Longitude
9902025-02-16 06:00:0011.174.103.117148.1.128.214Insider ThreatLow295655.9533-3.188345.5017-73.5673
9912025-02-16 07:00:0044.190.204.12321.66.42.61Insider ThreatHigh666751.5074-0.127849.2827-123.1207
9922025-02-16 08:00:0071.22.91.242109.106.166.96PhishingHigh732749.2827-123.120753.4808-2.2426
9932025-02-16 09:00:00109.60.240.20211.211.49.7DDoSCritical10860-33.8688151.2093-37.8136144.9631
9942025-02-16 10:00:0049.51.150.12454.91.252.232DDoSCritical1541449.2827-123.1207-37.8136144.9631
9952025-02-16 11:00:00188.129.228.85211.86.12.2RansomwareHigh139641.8781-87.6298-37.8136144.9631
9962025-02-16 12:00:00161.10.117.39163.126.132.81SQL InjectionMedium1928-27.4698153.025135.0116135.7681
9972025-02-16 13:00:0085.8.176.145101.19.239.116RansomwareMedium1654334.0522-118.243740.7128-74.0060
9982025-02-16 14:00:00193.13.249.52182.223.24.237DDoSHigh694034.6937135.502340.7128-74.0060
9992025-02-16 15:00:00211.50.176.100196.167.88.231Insider ThreatMedium254755.9533-3.188341.8781-87.6298